计算机应用 ›› 2013, Vol. 33 ›› Issue (09): 2694-2697.DOI: 10.11772/j.issn.1001-9081.2013.09.2694

• 典型应用 • 上一篇    下一篇

基于中心定位算法的细胞双光子显微图像分割

胡恒阳1,2,3,陈冠楠1,2,3,王平1,3,刘垚1,2,3   

  1. 1. 福建师范大学 光电与信息工程学院,福州 350007
    2. 医学光电科学与技术教育部重点实验室(福建师范大学), 福州 350007;
    3. 福建师范大学 光电与信息工程学院,福州 350007
  • 收稿日期:2013-04-04 修回日期:2013-04-25 出版日期:2013-09-01 发布日期:2013-10-18
  • 通讯作者: 陈冠楠
  • 作者简介:胡恒阳(1988-),男,江苏徐州人,硕士研究生,主要研究方向:数字图像处理;
    陈冠楠(1980-),男,福建福州人,副教授,博士,主要研究方向:医学图像处理、生物信号特征提取;
    王平(1955-),男,福建福州人,教授,主要研究方向:嵌入式系统、无线传感网络;
    刘垚(1989-),女,江苏徐州人,硕士研究生,主要研究方向:数字图像处理。
  • 基金资助:

    国家自然科学基金资助项目;福建师范大学优秀青年骨干教师培养基金

Segmentation of cell two-photon microscopic image based on center location algorithm

HU Hengyang1,2,CHEN Guannan1,2,WANG Ping1,LIU Yao1,2   

  1. 1. College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou Fujian 350007, China
    2. Key Laboratory of Optoelectronic Science and Technology for Medicine, Ministry of Education (Fujian Normal University), Fuzhou Fujian 350007, China;
  • Received:2013-04-04 Revised:2013-04-25 Online:2013-10-18 Published:2013-09-01
  • Contact: CHEN Guannan

摘要: 细胞双光子显微图像中存在边界模糊不清、噪声严重以及背景复杂等问题,使用现有方法提取边缘的结果不够理想。为此,提出了一种有效定位细胞核、提取边缘的新方法。采取由粗到精的分割策略,逐步提取出细胞核的边缘:首先,用C-均值聚类算法对图像进行分类,大致分出细胞核、细胞质和细胞间质三部分;然后,对分类结果的Canny边缘图进行圆形度计算,准确定位细胞核;最后,采用改进的水平集方法提取出细胞核的边缘。实验结果表明:对于背景复杂、干扰较多的细胞双光子显微图像,该方法可以精确地定位细胞核,所提取的细胞核边缘也较为精确。

关键词: 细胞图像, 图像分割, C-均值聚类, 圆形度, 水平集

Abstract: Complex background, critical noise and fuzzy boundary made the performance of the available cell image segmentation methods disappointing. Thus, a new method that can locate and detect nucleus effectively was proposed in this paper. A coarse-to-fine segmentation strategy was adopted to extract the edge of nucleus gradually. First, by using C-means clustering algorithm, the image was divided to three parts: nucleus, cytoplasm and cell intercellular substance. Second, the center of cell was located by calculating the circularity of Canny edge image. Finally, a reformed level set evolution was introduced to extract the edge of nucleus. The experimental results show that, nucleus can be located accurately; even if the cell image has a complex background and is disturbed by much stuff. Moreover, the edge of nucleus extracted by this method has a higher accuracy.

Key words: cell image, image segmentation, C-means clustering, roundness, level set

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